Da1yuqin/EviNoteRAG
Welcome! 😊 This is the official code release of EviNote-RAG, and we’re happy to share it with the community.
This project helps researchers and developers build more reliable AI systems for open-domain question answering. It improves how Large Language Models (LLMs) use retrieved information by having them generate 'evidence notes' that distill critical facts and identify uncertainties before answering. This results in more accurate and stable answers, especially when dealing with noisy search results.
Use this if you are developing or deploying AI systems for question answering and need to improve their accuracy and robustness by reducing the impact of irrelevant information from your search sources.
Not ideal if you are looking for a ready-to-use, no-code solution for general question answering without needing to customize or integrate with advanced retrieval systems.
Stars
45
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/Da1yuqin/EviNoteRAG"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
RapidAI/RapidRAG
QA based on local knowledge and LLM.
benitomartin/substack-newsletters-search-course
Production RAG System Course
liweiphys/layra
LAYRA—an enterprise-ready, out-of-the-box solution—unlocks next-generation intelligent systems...
LHRLAB/HyperGraphRAG
[NeurIPS 2025] Official resources of "HyperGraphRAG: Retrieval-Augmented Generation via...
limanmys/sef
On premise enterprise-grade RAG-powered agentic workflow chatbot platform with multi-provider support